1

NetWorks Senegal

Study on the efficacy of the sleeping-space registration strategy within the framework of LLIN distribution to

achieve universal coverage in Senegal (2011)

Final Report

2

With the contribution of

Funded through NetWorks

By USAID

NetWorks is financed by USAID under Cooperative Agreement No.GHS-A-00-09-00014-00. The

project is led by Johns Hopkins University Bloomberg School of Public Health Center for

Communication Programs (JHU•CCP) in collaboration with Malaria Consortium.

Disclaimer

The authors’ views expressed in this publication do not necessarily reflect the views of the

United States Agency for International Development of the United States Government.

Suggested citation:

Zegers de Beyl1 C.: Study on the efficacy of the sleeping-space registration strategy within the framework of LLIN distribution to achieve universal coverage in Senegal, February 2012.

1 Malaria Consortium International, Development House, 56-64 Leonard Street, London EC2A 4LT. UK.

3

Acknowledgements

We gratefully acknowledge all the people who contributed to this study. This survey was carried out by a steering committee, chaired by Prof. Ousmane Faye of Cheikh Anta Diop University and comprised of the following members: Dr Mame Birame Diouf (NMCP), Dr Mamadou Lamine Diouf (NMCP), Ouleye Beye (NMCP), Dr Babacar Gueye (NMCP), Dr Robert Perry (PMI), Dr Youssoufa Lo (NetWorks Senegal), Debbie Gueye (USAID), Joan Schubert (NetWorks Senegal), Medoune Ndiop (NMCP), Moussa Ndour (NMCP), Dr Abdoulaye Diop (NMCP), Dr Moustapha Cisse (NMCP), Dr Fatou Ba (NMCP), Samba Cor (NMCP), and Céline Zegers de Beyl (Malaria Consortium).

We gratefully acknowledge Dr Pape Moussa Tior, the outgoing NMCP Coordinator, for having undertaken and facilitated this study. We also thank Dr Cheikh Diop, NMCP Coordinator, for his involvement and guidance in presenting the results and writing this report.

We would like to express our thanks to Salif Ndiaye and his team at CRDH for their leadership in conducting the household surveys with a high level of professionalism as well as their sound technical advice. The dedication and professionalism of the surveyor teams, drivers, team leaders, and supervisors were crucial to the high quality of this study, and we extend our deep gratitude to them. We would also like to thank the government officials and local and religious leaders as well as the communities for their availability and collaboration during the fieldwork.

We gratefully acknowledge Professor Ousmane Faye for his technical support and unlimited availability throughout the entire process of this study. We would also like to thank Dr Lamine Diouf for facilitating the work completed by the steering committee.

We would like to thank Dr Robert Perry (PMI) for his technical support and availability during the preparatory stages of the study. We also express our deep gratitude to Dr Albert Kilian for his technical support during the design stage of the study tools and during the statistical analysis of data. We also thank those who contributed to writing this report: Dr Rachel Weber (NetWorks CCP), Julie Twing (PMI), Dr Youssoufa Lo (NetWorks Senegal), Aliou Fall (NetWorks Senegal), and Dr Matt Lynch (NetWorks CCP).

We extend our sincere gratitude to USAID for mobilizing the necessary funding to carry out this research project. We specifically would like to thank Debbie Gueye for her availability and support during the project.

Aliou Fall and Abdoul Aziz Mbaye of NetWorks Senegal facilitated the submission of the study protocol to the Research Ethics Committee and provided supervision during data collection. The focal points at NetWorks—Cheikh Sine (Sedhiou), Insa Badji (Kaolack), Ardo Faye (Tambacounda, Kaolack), Ousmane Ba (Kolda, Kaffrine), and Mamadou Lamine Gaye (Phase 2)—contributed to the development of this report by providing the operational expertise needed to accurately interpret these results. We warmly thank each of them.

Lastly, we would like to extend our thanks to Philippe Lambert who translated the English version of the protocol into French.

4

List of Abbreviations and Acronyms

BCC Behavior Change Communication

CRDH Centre for Research in Human Development

CHW Community Health Worker

DHS Demographic and Health Survey

FCFA French West African Franc

IRS Indoor Residual Spraying

ITN Insecticide-Treated Net

LLIN Long-Lasting Insecticidal Net

MICS Multiple Indicator Cluster Survey

MIS Malaria Indicator Survey

NMCP National Malaria Control Program

OMVS Organisation pour la mise en valeur du fleuve Sénégal (Senegal River Basin

Development Authority)

PMI President’s Malaria Initiative

UC Universal Coverage

USAID United States Agency for International Development

5

Table of Contents

SUMMARY ....................................................................................................................................... 7

INTRODUCTION ............................................................................................................................... 9

Context ..................................................................................................................................... 9

Objective/research questions/hypotheses ............................................................................ 9

Expected benefits ..................................................................................................................10

METHODS .......................................................................................................................................11

Study site ................................................................................................................................11

LLIN distribution ....................................................................................................................12

Operational definitions .........................................................................................................12

Design and sampling ..............................................................................................................12

Survey procedures .................................................................................................................14

Data management and analysis ............................................................................................15

Ethical considerations ...........................................................................................................15

RESULTS .........................................................................................................................................16

Description of sample............................................................................................................16

Registration for LLIN distribution ........................................................................................18

LLIN distribution process......................................................................................................21

Number of LLINs distributed compared to needs before distribution ...............................23

Household LLIN coverage the day of the survey ..................................................................26

Bed net/LLIN retention after distribution ............................................................................28

LLIN hang-up .........................................................................................................................29

Utilization of insecticide-treated bed nets the night before the survey ..............................32

Behavior change communication (BCC)...............................................................................41

DISCUSSION OF METHODS ............................................................................................................48

CONCLUSION .................................................................................................................................50

REFERENCES ...................................................................................................................................53

6

ANNEX............................................................................................................................................54

7

Sleeping-Space Registration Study to Achieve Universal LLIN Coverage, February 2012

SUMMARY

Context

In order to achieve universal LLIN coverage, Senegalese officials decided on an innovative strategy consisting of registering all households, nighttime sleeping spaces, and usable LLINs, and then allocating new bed nets to the sleeping spaces that had none, household by household. The objective of this study was to evaluate this distribution strategy. The goal of this research was to determine if universal coverage within a 10% margin was achieved through the sleeping-space registration strategy and if it effectively closed the gap in household coverage, with less than 10% of households over or under supplied.

Methods

The study targeted the population of the regions of Kolda, Sedhiou, Tambacounda, Kedougou, Kaolack, and Kaffrine, which were covered by the first two phases of the distribution. This was a cross-sectional study of households with a stratified two-stage cluster sampling design; 1560 subjects were sampled in 60 enumeration areas. The strata were defined by residential setting (urban/rural). The study was carried out by the Centre for Research in Human Development (CRDH). A pre-tested structured questionnaire was used to collect data; the interviews took place from 5 July through 4 August 2011. The data was double entered with EpiData software. Stata 11 software was used for the analysis and accounted for the design effect, weighting, and stratification. The research proposal was reviewed by the National Health Research Ethics Committee of the Senegalese Ministry of Health and Medical Prevention as well as the Institutional Review Board of the Bloomberg School of Public Health at Johns Hopkins University.

Results

The first two phases of the distribution were very effective in supplying households with

LLINs. The registration teams visited 90.5% of households, and the distribution participation rate was 92.0%, In total, 90.1% of households benefited from the distribution by receiving at least 1 LLIN, and 70.4% of households received enough LLINs to cover all their sleeping spaces on the day of the distribution. A total of 96.4% of the beneficiaries of the distribution hung up at least 1 LLIN within a week following distribution.

Distribution resulted in a significant increase (from 3.1% to 41.9%) in coverage for

households owning at least 1 LLIN per sleeping space; coverage for households having 1 LLIN per sleeping space on the day of the survey was 41.9%.

The retention rate for LLINs received during the distribution was 95.2% and that of older bed

nets was 49%.

On the day of the survey, the LLIN utilization rate was similar to that of retained older LLINs (82.5% versus 79.9%). Overall, 70.6% of sleeping spaces actually occupied the night before were protected with an LLIN.

Some 68.0% of the population slept under an LLIN the night before the survey. This

8

Sleeping-Space Registration Study to Achieve Universal LLIN Coverage, February 2012

proportion was 90.2% when only households that achieved universal coverage are considered. The proportion of LLINs shared by several persons was significantly higher among households with less than universal coverage (69.1% versus 51.4%), suggesting that the population adapts its coverage practices depending on the availability of LLINs.

Coverage of BCC activities was 75.2%. A total of 83.5% of respondents stated that they

intended to use their LLINs every night; 96.4% of households reported mosquito bites as the cause of malaria, and 92.6% reported using LLINs to prevent infection. Several barriers to LLIN use were identified, such as the belief on the part of one-third of the sample that the insecticide was harmful to children and pregnant women or that an LLIN was effective only with certain types of beds (60% of households).

Comparisons based on residential setting consistently showed more encouraging results

among rural households, indicating that it was significantly easier to implement distribution activities in villages. The results for the phase-2 regions were also consistently better, reflecting operational improvements resulting from capitalizing on lessons learned during phase 1.

Conclusion

The main objective was to determine whether the sleeping-space registration strategy resulted

in universal coverage within a 10% margin and if it effectively closed the gap in household coverage, with few households over or under supplied. This research demonstrated that mass distribution of LLINs was appropriate because nearly all households needed at least 1 LLIN in order to cover their sleeping spaces. Nevertheless, that resulted in a significant proportion of older LLINs that were discarded after the distribution (51.0%) compared to LLINs (4.8%). The first two phases of the distribution resulted in effective coverage of 70.6% of sleeping spaces the night before the survey. The trend observed was households with an under-supply of LLINs compared to the number of uncovered sleeping spaces.

9

Sleeping-Space Registration Study to Achieve Universal LLIN Coverage, February 2012

INTRODUCTION

Context

Distribution of LLINs has been recognized as one of the main interventions for malaria prevention, and mass distribution campaigns seem to be the best approach to quickly achieve a high level of coverage. However, Roll Back Malaria partners have not defined the optimal operational strategy for bed-net distribution to achieve universal coverage yet. Distribution of a fixed number of bed nets per household and bed-net distribution based on household size (for example, one bed net per three people in Madagascar) are among the strategies implemented in the past.

The Senegalese experience of universal coverage occurred in a context where large numbers of LLINs were already available in households. Therefore, Senegalese officials decided to use an innovative strategy that registered all households, sleeping spaces, and usable LLINs; allocated new LLINs to sleeping spaces that had none, household by household; and identified the LLINs distributed during the 2009 campaign for children under five years, previous campaigns, and routine distribution.

This study assesses the LLIN distribution strategy in the relevant regions by measuring the efficacy of this campaign and examining its various stages, including registration, distribution, and behavior change communication (BCC). Efficacy was measured in terms of household LLIN coverage as well as in terms of equitable distribution and use. The purpose of this research was to determine if the sleeping-space registration strategy effectively achieved universal coverage within a 10% margin and if it effectively closed the gap in household coverage, with less than 10% of households over or under supplied.

Objective/research questions/hypotheses

Overall objective

Assess the efficacy of the strategy to register nighttime sleeping spaces for LLIN distribution with a view to achieve universal coverage, defined as 1 LLIN for each sleeping space.

Specific objectives

Evaluate the census process;

Evaluate the distribution process;

Evaluate the efficacy of communication activities related to mass distribution; and Estimate the retention and loss rates for LLINs distributed through the campaign based on the

time elapsed since LLIN distribution.

10

Sleeping-Space Registration Study to Achieve Universal LLIN Coverage, February 2012

Hypothesis

By inventorying sleeping spaces and available and usable bed nets in each household, it is possible to achieve universal coverage, with few under-/over-supplied households, within a 10% margin.

Questions

Does this strategy effectively lead to universal coverage within a 10% margin? How effective were the communication activities during the distribution process?

Expected benefits

This study aims to achieve the following results:

Provide the National Malaria Control Program and the Regional and District Supervision Teams as well as Roll Back Malaria partners with data that quantifies the operational elements of distribution that might be difficult to anticipate;

Contribute to evaluating and guiding efforts to promote ITN use by presenting estimates on the level of knowledge and practices for malaria at the time of the survey;

Estimate the potential loss in coverage in the initial months following the campaign to demonstrate the possible need to strengthen routine distribution activities in these regions;

Contribute to measuring the strategy’s cost-effectiveness by incorporating cost data; Contribute to demonstrating which indicators measuring universal coverage at the

household level (the ratios: LLIN/person and LLIN/sleeping space) are the most relevant in terms of intra-household access in the study regions; and

Inform the international community in the fight against malaria about the Senegalese experience and its sleeping-space registration strategy to achieve universal LLIN coverage.

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Sleeping-Space Registration Study to Achieve Universal LLIN Coverage, February 2012

METHODS

Study site

The study targeted the population in the regions of Senegal covered by universal coverage activities. These regions are: Kolda, Sedhiou, Tambacounda, Kedougou, Kaolack, and Kaffrine (Figure 1); they border The Gambia, Guinea-Bissau, Guinea, and Mali. The target population is estimated at 2,925,388,2 distributed over more than 30% of the Senegalese territory.3 The regions covered by phase 1, located in the southern part of the country (Sedhiou, Kolda, Tambacounda, and Kedougou) are more rural than the regions of Kaffrine and Kaolack, covered by phase 2 and located north of The Gambia. The 2002 census report estimates the population density at 39/km2 in the regions of Sedhiou and Kolda, 10/km2 in the regions of Tambacounda and Kedougou, and 69/km2 in the regions of Kaolack and Kaffrine.4

Figure: Target population of the study

The regions in this study are characterized by sub-humid tropical climate. There are two distinct seasons: the dry season from November to April and the rainy season, or hivernage, from May to October. Average annual precipitation ranges between 600 and 1100 mm (Sudanian climate) in the north of the study regions and from 1100 to 1500 mm (Guinean climate) in the south. Vegetation becomes increasingly dense from the north to the south and is characterized by

2 Distribution activities data. 3 Final results from the third General Census of Population and Housing (2002), National Agency for Statistics and Demography (ANSD in French), National Report Presentation, June 2008. 4 Redistricting in 2008 created 3 new regions, with the establishment of Kaffrine (Kaolack region), Sedhiou (Kolda region), and Kedougou (Tambacounda region) into regions. The total number of regions increased from 11 to 14.

12

Sleeping-Space Registration Study to Achieve Universal LLIN Coverage, February 2012

wooded savannah in the north and forest in the south. Additionally, temperatures are influenced by oceanic air and increase from west to east. The data for this study were collected at the end of the dry season, before the first rains had fallen.

LLIN distribution

Activities for universal LLIN coverage in Senegal were designed and coordinated by the National Monitoring Committee, chaired by the National Malaria Control Program. This committee was made up of the following partners: NetWorks, PMI, IntraHealth, ChildFund, Senegalese Red Cross, Caritas, Secours Islamique, Peace Corps, and OMVS. Four committees were also established to oversee communication, monitoring and evaluation, logistics, and technical aspects.

Activity Calendar

Activity Start date End date

Phase 1 Phase 2 Data collection

19 May 2010 6 Dec. 2010 5 July 2011

13 Oct. 2010 17 Mar. 2011 4 Aug. 2011

The goal of the distribution activities was to cover every bed or sleeping space with an LLIN. In order to take into account existing coverage resulting from previous distributions, LLIN needs were estimated by community health workers who made home visits to inventory the sleeping spaces and existing and usable LLINs, household by household. The following formula was used to calculate needs: “No. of sleeping spaces – No. of usable LLINs.”

Operational definitions

The following definitions have been used for distribution activities and for this study:5

Nighttime sleeping space: the usual space for sleeping at night; beds/sleeping spaces placed in the home’s courtyards and used occasionally must therefore be excluded from the inventory. If someone occasionally sleeps outdoors, the LLIN must be detached and hung up over his or her sleeping space.

Household: under the entity of concession in some regions; it can be composed of a head of household, his wife or wives and his children. A single person renting a room or an apartment in a concession can constitute a household.

Non-usable LLIN: torn bed net with unrepairable holes.

Design and sampling

This study is a cross-sectional survey of households with a stratified two-stage sampling design.

5 Senegalese National Malaria Control Program Methodology Guide, 2010

13

Sleeping-Space Registration Study to Achieve Universal LLIN Coverage, February 2012

The total population of the 6 relevant regions was taken into account, with the cluster defined as

an enumeration area (= subdivision of a commune or sub-prefecture in a homogeneous territorial

unit). The strata were defined by the residential setting of the households (classified rural/urban according to the 2002 census). The sampling method was designed to obtain a representative

sample of the population from the study regions. This method also allows for inclusion of

enumeration areas or households potentially not covered by the distribution.

In order to measure the proportion of sleeping spaces covered by a bed net and to compare the urban to the rural community, the sample size was calculated by taking into account the following hypotheses:

Confidence interval (alpha error) 95%

Power (beta error) 80%

Design effect of 1.75

Non-response rate of 10%

Thus, it was estimated that a total sample of 60 clusters of 26 households each (1560 households) would give an accuracy of 5% if the percentage of sleeping spaces covered by bed nets was 50%; the accuracy would be 4% if estimated at 80% in each stratum. Considering that 1–2 people per household are interviewed, based on the head of household or another family member’s participation in the distribution, the total and maximum sample size for participants is 3120 (1560*2).

Each stratum was considered a survey domain in which 30 clusters were selected, in compliance with the standard methodology used in Demographic and Health Surveys and Malaria Indicator Surveys. Hence the rural setting was over-represented relative to the actual proportion in order to obtain results with a similar degree of accuracy by residential setting and to make comparisons. This was later corrected in the analysis so that the results were representative of the entire population.

First degree: cluster selection Results from the 2002 census were used to select the clusters, and current population figures were estimated. The clusters were selected in the sampling frame with probability proportional to size. In each stratum, the enumeration areas were chosen using a list of all the areas with cumulative population data; 30 enumeration areas were selected by systematic sampling with probability proportional to size. The selected clusters were mapped using a geographical positioning system (GPS) receiver.

Second degree: household selection In each of the selected enumeration areas, 26 households were selected based on a simple random sample. The definition of household is identical to that used in the LLIN distribution campaign, in other words, the head of household and his or her family.

14

Survey procedures

The study was implemented by the Centre for Research in Human Development (CRDH).

Questionnaire

A structured questionnaire, based on questionnaires that were pre-tested and used in previous surveys, was used to collect data. The main respondents were the head of household or his or her representative and the recipient of the LLIN at the distribution site. One questionnaire module included questions about the existing LLINs in the household; each one was directly observed by the survey team, with authorization from the head of household.

Visual aid

In order to identify the various LINN brands and to categorize them by type of bed net (LLIN, ITN, untreated nets), surveyors were provided with visual aids: photos of the various brands of LLINs with their packaging. If the surveyor was refused access to the household’s existing bed nets, he or she asked the respondent to identify the brand of the LLIN with the help of the visual aid.

Teams and training

Surveyors were selected on the basis of their being accepted by communities, their command of the local language, and their experience in conducting household surveys. Interviews were conducted by 5 teams, each composed of 4 surveyors and 1 supervisor. The week before data collection, the survey teams received training on the study method and procedures for household interviews. This training included role-plays as well as pilot interviews.

Community mobilization

Authorization to conduct the survey was requested from local officials. Leaders in the enumeration areas were visited to explain the study’s goal and procedures; neighborhood leaders were then responsible for informing the community. Once authorization to conduct the household interviews was granted, the mobilizers agreed on the date the survey team would come. Community mobilization also sought to ensure that the surveyor visits would not raise expectations for another distribution.

Household interviews

The interviews took place from 5 July to 4 August 2011. Each selected household received a visit from a surveyor, and the head of household or his or her representative was surveyed. If none of the present household members was able to represent the head of household, a future visit was scheduled. In the event that nobody was home, a total of three visits were attempted to meet with a respondent before dropping the household without replacement. Supervision and quality control

At the end of each day, the supervisors were responsible for reviewing all of the completed questionnaires in order to verify the completeness of data and indentify possible inconsistencies.

15

Hence, missing or erroneous information could be completed or corrected before leaving the field. Moreover, random checks were conducted for each of the surveyors.

Data management and analysis

Data entry was done in Dakar using Epidata software. All findings were double entered, and the two databases were then compared. Potential conflicting findings were checked using the original questionnaire. Then, all data were transferred to CS Pro statistical software with a new unique identification number to identify individuals and households. A second step to clean data was meticulously conducted, and this process was documented.

The survey was analyzed using an “intention to treat” approach; in other words, all sampled households were included. All analysis accounted for the study methodology, particularly the design effect, weighting, and stratification. In addition to the univariate analysis, multivariate modeling using logistic regression was performed to investigate determinants of critical variables of interest such as retention, hang-up, and use of bed nets.

Ethical considerations

Participation in the study was completely voluntary and participant confidentiality was conscientiously maintained throughout the process. Verbal consent from each respondent was obtained and documented by the surveyors prior to interviews. The research proposal was reviewed by the National Health Research Ethics Committee of the Senegalese Ministry of Health and Medical Prevention (Authorization Number: 000089/MSP/DS/CNERS/2011) as well as the Institutional Review Board (IRB) of Bloomberg School of Public Health at Johns Hopkins University (Authorization Number: 00003351).

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RESULTS

Description of sample

The goal was to sample 1560 households; 13 households were absent for three consecutive visits and 6 households refused to participate. Therefore, the sample size was 1541 households, or 98.8% of the initial goal. In each phase, the percentage of households in an urban setting was similar (49.0% in phase 1 and 48.41% in phase 2).

Table 1: Characteristics of sampled households Characteristics Households

with children

under 5 years

(%)

Households

with a

pregnant

woman (%)

Average

number of

sleeping

spaces per

household

Average

number of persons per

household

Number of

households

Residential setting

Urban

Rural

59.2

85.7

14.1

18.7

4.97

6.59

8.55

11.82

765

776

Phase of UC

First

Second

78.2

83.1

15.8

19.7

6.40

6.20

11.61

10.85

741

800

Wealth quintile

Poorest

Second

Average

Fourth

Richest

84.1

81.4

82.2

79.6

77.2

16.6

9.0

17.6

26.7

18.1

6.28

6.02

6.07

6.45

6.60

11.35

10.79

10.99

11.47

11.42

308

307

309

308

309

Overall 80.8 17.8 6.29 11.21 1541

Households in rural settings had more people (11.8 versus 8.55) and were more likely to include a child under five years (85.7% versus 59.2%) or a pregnant woman (18.7% versus 14.1%) compared to households in urban settings. Similarly, there were also more sleeping spaces (6.59 versus 4.97).

Figure 1: Age pyramid of the population living in the sampled households INSERT FIGURE 1

The composition of the population living in the sampled households was similar to that of populations in Sub-Saharan African countries with 18.4% of children under five years and 47.8% of persons under 15 years. The comparison between residential settings showed the same previously observed trend; in other words, greater proportions of youths in rural households. Furthermore, pregnant women accounted for 1.8% of the sample.

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Table 2: Characteristics of sleeping spaces in the sample Characteristics Among all sleeping spaces Among occupied sleeping spaces

Sleeping spaces

occupied

the night before (%)

Average number of

persons

per sleeping

space

Number of

sleeping

spaces

Average number of

persons

per sleeping

space

Sleeping spaces

located

outside (%)

Beds (%) Number of sleeping spaces

Residential setting

Urban

Rural

92.2 94.9

1.72 1.82

3907 5245

1.75 1.81

3.3 2.4

75.9 87.3

3612 4957

Phase of UC

First

Second

93.4

95.6

1.82

1.79

4475

4677

1.85

1.76

5.1

0.1

80.1

90.0

4132

4437

Wealth quintile

Poorest

Second

Average

Fourth

Richest

94.3

95.2 94.2

95.0

94.0

1.86

1.80 1.85

1.79

1.72

1781

1756 1802

1908

1905

1.85

1.80 1.84

1.79

1.76

2.8

3.0 1.5

1.0

4.2

84.7

88.0 85.4

87.7

83.0

1692

1651 1686

1792

1748

Overall 94.5 1.80 9152 1.81 2.5 85.7 8569

For the 9152 sleeping spaces identified by respondents, 94.5% were occupied the night before the survey, and the average number of persons per sleeping space was 1.80. When only accounting for the sleeping spaces occupied the night before the survey, 2.5% were located outdoors and 85.7% were beds; 10.8% of the sleeping spaces consisted of mattresses on the ground and 3.5% were mats or sheets.

It was noted that the number of persons per sleeping space seemed to decrease as economic well-being increased. This could be explained by the higher rate of polygamy in rural settings. A higher average number of persons per sleeping space was observed in households in regions covered by phase 1.

In addition, in rural settings, sleeping spaces had a significantly higher tendency to be occupied by an adult female and a child under five years in comparison to urban settings. By contrast, a higher percentage of sleeping spaces was occupied by an adult couple (male/female) in urban settings compared to rural settings. This is explained by the high proportion of polygamous households in rural settings where each of the wives lives in a separate hut with her children. On the other hand, in urban settings, the rural exodus and lack of space have meant that there are significantly fewer polygamous families than in the country.

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Registration for LLIN distribution

The household registration step carried out by community health workers made it possible to estimate LLIN needs by counting the number of sleeping spaces per household and the number of existing LLINs received before the distribution. The households that needed to participate in the distribution received a voucher allowing them to pick up LLINs from the distribution site in the days following registration.

Table 3: Household registration by cluster Registered households

Residential setting Total

Urban Rural

None - - 0/60

1%–50% - - 0/60

51%–80% 9/30 (30%) 2/30 (6.7%) 11/60 (18.3%)

>80% 21/30 (70%) 28/30 (93.3%) 49/60 (81.7%)

For all of the 60 household clusters sampled, 81.7% of them had a registration rate higher than 80%. Stratification by residential setting indicates that a higher percentage of clusters with a registration rate above 80% was found in rural settings; by contrast, the clusters that had a registration rate between 51% and 80% were primarily located in urban settings.

Table 4: Results of registration-team visits

Characteristics Households visited

by the registration

team (%)

Among visited households

(N=1356)

Households that received a voucher among households needing at least 1

LLIN (%) (N=1437)

Households where all

sleeping spaces were

registered (%)

Households that received a

voucher (%)

Residential setting Urban

Rural

81.3

92.5

92.3

97.1

89.0

86.0

73.9

80.0

Phase of UC

First

Second

85.2 95.4

94.5 97.8

76.7 94.7

66.2 91.1

Household size 1–7

8–11

12 or more

83.7

93.4

93.8

96.4

95.6

96.9

87.0

89.6

83.6

74.1

84.0

78.7

Wealth quintile

Poorest

Second Average

Fourth

Richest

87.5

93.5 87.8

94.5 88.8

98.2

97.1 95.6

95.0 96.2

85.8

87.7 81.3

88.6 88.3

75.5

82.2 71.6

84.7 79.9

Overall 90.5 96.3 86.5 79.0

19

Overall, 90.5% of households were visited by the registration team before distribution, which is the registration coverage rate. In 96.3% of these 1356 households, the team registered all sleeping spaces, based on respondents, and 86.5% received a voucher to pick up their LLIN at the distribution site. As a result, the registration efficacy rate reached 79.0%, equal to the percentage of households that needed at least 1 LLIN from the distribution and received a voucher.

By comparing residential settings, it is apparent that the coverage rates (92.5% versus 81.3%) and the registration rate for all sleeping spaces (97.1% versus 92.3%) were more encouraging in rural settings. Additionally, households with less than 8 persons were significantly less likely to be visited by the registration teams (83.7% versus 93.4% and 93.8%). These trends are probably a reflection of the greater availability of households in rural settings in addition to larger families and greater acceptance of community health workers in rural settings. Lastly, coverage for registration visits was higher during phase 2 (95.4% versus 85.2%), and these households were also more likely to receive a voucher (94.7% versus 76.7%), reflecting improvements in the strategy between the two phases.

Table 5: Reasons for non-registration

Characteristics Among non-registered households (N=185) Households with

at least 1 new

sleeping space since registration

(%)

(N=1541)

Team didn’t come (%)

We were absent (%)

We arrived after the

campaign (%)

Other reason or reason unknown

(%)

Residential setting

Urban Rural

30.0 45.6

39.1 32.3

9.0 4.3

21.9 17.8

2.7 1.0

Phase of UC

First

Second

45.6 23.0

29.6 50.1

6.0 6.0

18.7 21.0

1.6 1.0

Wealth quintile

Poorest Second

Average

Fourth Richest

47.2 33.5

31.6

65.1

31.9

31.8 44.3

36.7

16.7

39.6

7.4 4.7

0.0

5.0

11.6

13.6 17.5

31.7

13.1

17.0

0.7 1.9

1.4

1.4

1.1

Overall 39.9 34.8 6.0 19.3 1.3

Households in rural settings as well as those surveyed in phase 1 had a greater tendency to mention a reason involving the team, whereas in urban settings and phase 2, households that did not receive team visits instead tended to have been absent when the team came. These differences suggest an improvement in registration team coverage in phase 2 (23% compared to 45.6% of households not visited mentioned the teams absence as reason).

Next, these results suggest that in rural settings, the main challenge for registration teams is village accessibility while in urban settings, it was the household’s absence during the day, since members were probably at work.

20

Registration stage: improvements in the strategy between phases 1 and 2

Training for community health workers was strengthened. On the one hand, a session was devoted specifically to the poorly defined registration units. On the other hand, training for the second phase strongly emphasized how to pose questions to heads of household; rather than directly asking “How many sleeping spaces does this household have?” the community health worker was instead encouraged to ask “Can you show me your sleeping spaces?

Next, supervision capacities were improved during phase 2. By involving several partners, more supervisors could be deployed for more days. Strengthening support for community health workers promoted greater acceptance of them in households and made them more effective in estimating LLIN needs. The supervision teams also used quick registration check sheets to ensure better census coverage.

Moreover, the issue of seasonality should be taken into account during implementation. Phase 1 was rolled out during the rainy season, which meant there were problems with road access and a lack of availability for some households whose members were working in the fields.

Lastly, in the first phase, community health workers were instructed to downgrade the LLINs according to the pre-defined criteria in the Methodology Guide. These criteria were: LLIN brand, lifespan of less than 5 years, an LLIN washed more than 20 times, an LLIN not exposed to sunlight, and a torn or unrepairable LLIN. As a result, the estimated LLIN needs during phase 1 reflected each community health worker’s own interpretation of these criteria. In phase 2, the criteria were simplified as follows: LLIN brand and torn or unrepairable LLIN. The potential effect of this improvement has not been identified by this study. This could be due to a greater number of downgraded LLINs that had been discarded since the distribution.

In conclusion, taking into account the lessons learned during phase 1 led to improved implementation of the registration strategy. As a result, significantly higher coverage of households by the registration teams can be noted in phase 2 (from 85.2% to 95.4%). Nearly all the visited households in phase 2 reported that all of their sleeping spaces had been identified (97.8% compared to 94.5%), and the percentage of households that received a voucher went from 76.7% in phase 1 to 94.7% in phase 2. Hence, the registration step enabled targeting 91.1% households needing LLINs in phase 2, an increase of 25% since phase 1.

21

LLIN distribution process

On the day of distribution, a household member went to a distribution site with his or her voucher to exchange it for the number of LLINs deemed necessary during registration to cover all sleeping spaces in the household. The instructions were to distribute the LLINs out of their packaging.

The recipient of the LLIN at the distribution site was a household member who was reachable the day of the survey for 73.6% of households that participated in the distribution. For all households in the sample, very few, or 5.2%, did not need any LLINs, in that they already had at least 1 LLIN per sleeping space. More of these households were found in urban settings than in rural settings.

Table 6: Households that participated in distribution and received LLINs

Characteristics Went to the

distribution

site (%)

Received at least 1 LLIN

Among those who went to the

distribution site

(%) (N=1370)

Among all households in sample (%)

Among households needing at least 1

LLIN (%)

(N=1437)

Residential setting

Urban

Rural

81.3 94.4

94.4 97.4

76.8 92.0

78.5 92.5

Phase of UC

First

Second

88.0 95.6

95.2 98.5

83.8 94.1

84.7 95.2

Wealth quintile

Poorest

Second

Average

Fourth

Richest

90.8 95.2

90.0

95.8

88.1

96.4 97.2

97.3

97.6

96.2

87.5 92.5

87.5

93.5

84.7

88.0 92.9

87.6

94.7

86.8

Overall 92.0 96.9 89.1 90.1

Overall, the participation rate was encouraging with 92.0% of households having gone to the distribution site. This percentage was significantly higher in rural settings (94.4% versus 81.3%) as well as among households covered by phase 2 (95.6% versus 88.0%).

The distribution process at the sites was highly effective, with 96.9% of households present having received at least 1 LLIN. A much higher percentage was noted in rural settings (97.4% versus 94.4%), probably reflecting easier set-up of distribution sites there than in urban settings. Similarly, the households that were present at the distribution sites in phase 2 were more likely to benefit from the distribution (98.5% versus 95.2%).

As a result, 89.1% of the sample benefited from the distribution. Thus, the distribution efficacy rate was estimated by excluding the households that already had at least 1 LLIN per sleeping space before distribution. Overall, distribution covered 90.1% of households that needed LLINs; this efficacy rate reached 95.2% in phase 2 and 92.5% in rural settings.

Table 7: Average number of LLINs received per household

22

Characteristics Average, among households that received at least 1 LLIN

(N=1318)

Average for the entire sample

Residential setting Urban

Rural

3.89

4.60

2.98

4.23

Phase of UC

First

Second

4.46 4.51

3.73 4.25

Household size 1–7

8–1

12 or more

2.60 3.91

6.28

2.09 3.54

5.98

Wealth quintile

Poorest

Second Average

Fourth

Richest

4.67 4.38

4.00

4.48

4.87

4.08 4.05

3.50

4.19

4.12

Overall 4.49 4.00

The households that benefited from the distribution received, on average, 4.49 LLINs per household. The number of LLINs received was dependant on the household’s size and residential setting, since rural households are larger than urban households. On the other hand, it is interesting to note that the number of LLINs received was higher in phase 2, reflecting the lack of LLIN availability in phase 1.

Of the 1004 respondents who went directly to the distribution site, 98.2% spent no money to go pick up their LLINs; 18 (1.8%) had to spend 555 FCFA, on average, for transportation.

The travel time needed to get to the distribution site was estimated to be at least 30 minutes for 70.1% of households, and the estimated travel time was at least one hour for 97.7%, suggesting that the sites were appropriate in terms of proximity to beneficiaries. By contrast, LLIN recipients seemed to have waited longer at the distribution site, with over half, or 69.9%, estimating the wait time at less than one hour and 99.4% at less than two hours.

Figure 2: How LLINs were distributed

23

Lastly, 73.2% of beneficiaries reported that the LLINs received during distribution either had no packaging (59.5%) or open packaging (13.7%) while 20.1% received their LLINs with the packaging intact, contrary to the instructions. A significant increase in the number of beneficiaries who received their LLIN without packaging was noted among households covered by phase 2.

Number of LLINs distributed compared to needs before distribution

The goal of the distribution was to cover all sleeping spaces with LLINs. The number of LLINs to distribute was estimated during registration by counting both the number of sleeping spaces and existing LLINs.

Table 8: Results from the distribution relative to the gap in LLINs before distribution Characteristics Among households that needed at least 1 LLIN and that benefited from the

distribution; N=1247 (%)

Needed at

least 1 LLIN but did

not benefit

from the

distribution

(%) (N=1437)

Received

necessary

quantity,

within a

10% margin

(%)

Received

necessary

quantity,

within a 1-

LLIN margin

per

household

(%)

Received at

least 2 LLINs less

than needed (%)

Received at

least 2 LLINs in excess (%)

Have received

enough LLINs

to cover all

sleeping

spaces

(%)

Residential

setting Urban

Rural

38.5

36.0

58.4

59.6

26.0

30.3

15.6

10.2

74.0

69.8

21.6

7.7

Phase of UC

First

Second

32.6

40.0

57.6

60.9

36.0

24.2

6.4

14.9

64.0

75.8

15.5

4.9

Household size

1–7

8–1

12 or more

39.9 31.6

37.4

73.5 58.3

50.7

19.0 26.6

39.1

7.5 15.0

10.2

81.0 73.3

60.9

18.5 9.2

4.1

Wealth quintile

Poorest

Second

Average

Fourth

Richest

40.3 41.8

34.7

34.1 32.2

63.8 63.7

62.7

56.6 50.9

27.5 24.9

30.7

31.0 33.8

8.7 11.4

6.6

12.4 15.3

72.5 75.1

69.3

69.0 66.2

12.0 7.7

12.4

5.3 13.3

Overall 36.4 59.7 29.6 11.0 70.4 10.1

* Estimation of needs calculated using the number of bed nets present in the household, minus those reported as

discarded due to excessive damage (800 bed nets).

Overall, 70.4% of households received enough LLINs to cover all their sleeping spaces, within a margin of 1 LLIN per household; 29.6% received at least 2 LLINs less than needed before the distribution, and 10.1% did not benefit from the distribution.

24

Several trends emerged from this analysis. Firstly, small households (less than 8 people) were more likely to receive the correct number of LLINs relative to their needs (73.5% versus 58.3% and 50.7%) while larger households (over 7 persons) tended to receive an insufficient number of LLINs (26.6% and 39.1% versus 19.0%). This observation is also reflected by residential setting in that more rural, and therefore larger, households received fewer LLINs than they needed (30.3% versus 26.0%).

Also, in phase 2, the proportion of households that received the correct quantity was 60.9% versus 57.6% in phase 1. Similarly, significantly fewer households in phase 2 had received an insufficient number relative to their needs, or 24.2% compared to 36.0% in phase 1. Consequently, during the second phase, 75.8% of households received enough LLINs to cover all their sleeping spaces, compared to only 64.0% in phase 1.

Lastly, 10.1% of households received no LLINs. The main reason why these households did not benefit from the distribution was lack of registration for 54.1% of these households while in only 23.9% of cases, the households were registered and present during the distribution. This last scenario was primarily observed during phase 1, particularly in Tambacounda, which did not have a sufficient stock of LLINs.

This analysis suggests that smaller households are less available, both for registration and the distribution, yet more likely to receive enough LLINs. This is reflected in the higher proportion of excluded households among the families with less than 8 people (18.5% versus less than 10% for households of more than 7 people) as well as among urban households (21.6% versus 7.7%). Moreover, the lack of available LLINs in phase 1 may partially explain the significantly higher proportion of excluded households during phase 1 (15.5% versus 4.9%).

25

Distribution process: improvements in the strategy between phases 1 and 2

Firstly, during the first phase, the gap in LLIN ownership was estimated using data from the 2002 population census, readjusted for the population growth rate in Senegal. By contrast, during the second phase, estimations of the number of LLINs to distribute were improved by introducing a validation step for registration data during the second phase. Through this measure, the number of LLINs to be distributed could be readjusted if necessary, ensuring greater availability of lLINs at each site.

Secondly, distribution planning was strengthened significantly between the two phases. The number of distribution sites was increased with greater attention to factors of accessibility, community acceptance, and population density during phase 2. Next, organization at each site was improved, for example, by regularly preparing batches of LLINs to distribute per household the day before or by assigning time slots to households to avoid a massive influx of beneficiaries. Although these measures were also applied in some districts in phase 1, implementation in phase 2 was much more standardized.

Thirdly, the communication strategy was significantly improved by applying lessons learned during the first phase. The communication activities were standardized in phase 2 at the central level. Also, providing training for traditional communicators strengthened outreach communication overall. Involving the community through monitoring committees made up of influential community members was also a key factor in the success of phase 2.

Fourthly, the aforementioned factor of seasonality has also influenced distribution implementation. Phase 2, which took place in the dry season, had more favorable weather conditions for distribution activities, ensuring better access to transportation and greater availability of households, particularly in rural settings.

These improvements in the strategy resulted in a higher participation rate in phase 2 (95.6% compared to 88.0%), a higher percentage of recipients that received at least 1 LLIN among those present at the site (98.5% compared to 95.2%), a higher percentage of households that received enough LLINs to cover all their sleeping spaces (75.8% compared to 64.0%), fewer households excluded from the distribution in phase 2 (4.9% compared to 15.5%), and lastly, a higher average number of LLINs distributed to households (4.25 compared to 3.73).

26

Household LLIN coverage the day of the survey

Table 9: Coverage for households with at least 1 bed net/LLIN before distribution and on the day of the survey Characteristics Households having at least 1 bed

net

Households having

at least 1 LLIN

Before

distribution (%)

Day of the survey

(%)

Day of the survey

(%)

Residential setting

Urban Rural

36.3 40.7

92.1 97.4

87.4 95.4

Phase of UC

First

Second

31.3 47.9

94.5 98.3

89.7 97.8

Wealth quintile

Poorest

Second

Average

Fourth

Richest

36.8 37.5

44.5 43.4

37.1

98.4 97.0

96.6 97.3

93.4

93.0 96.3

93.9 96.5

89.9

Overall 39.9 96.5 93.9

Coverage for households that had at least 1 bed net before the distribution was 39.9%. On the day of the study, this percentage had reached 96.5%. Therefore, distribution led to an increase in coverage of 56.6%. Lastly, coverage for households having at least 1 LLIN was 93.9% the day of the survey. Coverage for households having at least 1 bed net or LLIN was higher in rural settings and also in the phase 2 regions, before distribution and on the day of the survey.

Table 10: Coverage for households that have achieved universal coverage Characteristics Households having 1 LLIN per

sleeping space (%)

Household having 1 LLIN per person

(%)

Before distribution (bed net coverage)

On day of survey Before distribution (bed net coverage)

On day of survey

Residential setting

Urban

Rural

6.4

2.3

44.8

41.2

5.8

2.1

43.5

41.6

Phase of UC

First

Second

3.0 3.1

35.2

48.2

3.0 2.7

32.6 50.6

Wealth quintile

Poorest Second

Average

Fourth

Richest

3.1 0.9

3.0

2.0

6.0

47.2

47.0

38.4

38.9

38.9

2.6 1.2

3.3

2.1

4.6

42.8 48.8

33.6

43.4

40.9

Overall 3.1 41.9

2.8 41.9

A very limited proportion of households had at least 1 bed net per sleeping space before distribution, or 3.1%. This percentage is significantly higher in urban settings (6.4% versus 2.3%) as well as among the richest households (6% versus less than 3% for the 4 other quintiles).

27

On the day of the survey, 41.9% of households had at least 1 LLIN per sleeping space. Similar to the trends observed for coverage before distribution, urban households and households in phase 2 were more likely to have their sleeping spaces covered. By contrast, the observed trend by wealth quintile was the reverse, namely that on the day of the study, percentages for households that had 1 LLIN per sleeping space were higher among the poorest households (47% versus less than 39% among the richest households). Lastly, very few households, or 2.8%, had at least 1 bed net per 2 people before the distribution while on the day of the survey, this percentage had reached 41.9%, meaning an increase in coverage of 39.1%.

It is useful to consider the proportion of households having almost enough LLINs to cover all their sleeping spaces, or 1 LLIN less than their number of sleeping spaces. Overall, the percentage of households having almost 1 LLIN per sleeping space was 59.7%. It seems reasonable to assume that in nearly 60% of households, people could have access to an LLIN, even if it meant altering their sleeping habits.

Table 11: Average number of LLINs per household, per sleeping space, and per person on the day of the survey Characteristics Number of

LLINs per

household

Number of LLINs per sleeping space Number of LLINs per person

All sleeping spaces

Sleeping spaces occupied the previous night

For the entire population

Persons present the previous night

Residential setting

Urban Rural

3.43 4.86

0.70 0.75

0.77 0.80

0.44 0.43

0.49 0.47

Phase of UC

First

Second

4.12

5.04

0.64

0.83

0.70

0.88

0.37

0.49

0.41

0.54

Wealth quintile

Poorest Second

Average

Fourth

Richest

4.71

4.63

4.21

4.71

4.68

0.77

0.77

0.71

0.75

0.70

0.82

0.82

0.76

0.81

0.75

0.44

0.45

0.40

0.44

0.43

0.47

0.48

0.44

0.49

0.50

Overall 4.59 0.74 0.79 0.43 0.48

On the day of the study, the average number of LLINs per household was 4.59. The average number of LLINs per sleeping space was 0.74; when excluding the sleeping spaces that were unoccupied the night before the survey, the average number of LLINs per sleeping space was 0.79. Moreover, the number of LLINs per person was 0.43, but when excluding those who were absent the night before the household interview, the number is 0.48.

It was noted that the number of LLINs was higher in rural settings; the opposite trend seen for the ratio of LLINs per person reflects the significantly smaller size of households in urban settings. Moreover, the number of available LLINs on the day of the survey was significantly higher in phase 2, illustrating the greater quantity of LLINs distributed in these regions.

28

Bed net/LLIN retention after distribution

For the 7068 existing bed nets in households the day of the survey, 94.0% were LLINs, 5.8% were non-insecticide-treated nets, and only 0.2% were ITNs (excluding LLINs).

Table 12: Bed-net retention since the last distribution Characteristics Retained bed nets/LLINs Retention of LLINS from the distribution, for

households having received at least 1 LLIN (N=1318)

No LLINs (%)

Some

LLINs (%)

All

LLINs (%) LLINs from

the distribution

(%)

Bed nets

procured before distribution (%)

Residential setting

Urban Rural

91.0

95.9

45.7

49.6

3.7

1.2

15.4

11.7

80.8

87.1

Phase of UC

First

Second

93.4

96.7

46.3

50.9

2.4

0.9

14.4

10.5

83.2

88.6

Wealth quintile

Poorest Second Average

Fourth

Richest

97.3

95.5

94.4

94.6 94.4

53.1

53.1

51.4

49.1 41.6

1.1

1.7

0.1

1.6 3.2

9.1

12.8

14.4

12.8 12.2

89.8

85.5

85.4

85.6 84.6

Overall 95.2 49.0 1.6 12.3 86.1

The retention rate for LLINs distributed 3 to 12 months after distribution was 95.2%. This rate was higher for rural households (95.9% versus 91.0%). Moreover, a greater percentage of LLINs had been retained in phase 2 (96.7% versus 93.4%), which is consistent with the most recent distribution period. Lastly, the proportion of retained LLINs tended to be smaller among the richest households.

By contrast, only 49.0% of bed nets that households had before the distribution were still found in the households on the day of the survey. The same trends as for LLINs from the distribution were observed; it seems that the richest households were more likely to get rid of their older bed nets than their new LLINs (difference of de 11.5% versus 2.9%).

The majority of households, or 86.1%, kept all the LLINs they received during the distribution and only 1.6% kept none. Households in rural settings and households in phase 2 seemed more likely to keep their LLINs. This could be a reflection of the outreach communication activities, which are easier to implement in rural settings and were improved during phase 2. Figure 3: Reason for non-retention of LLINs from the distribution (N=338) and of old nets (N=1482)

INSERT FIGURE 3 This figure shows that 74.5% of the older bed nets that were no longer in households had been intentionally discarded (given or thrown away or used by another user) compared with only 53.2% of

29

the LLINs from the distribution. This difference is due to the much greater proportion of older bed nets that were damaged, as shown by the figure below.

Figure 4: Reasons mentioned for non-retention of bed nets

INSERT FIGURE 4

This analysis presents the reasons for non-use, comparing the older bed nets and the LLINs from the distribution. These reasons were mainly objective for the older nets at the time of distribution; 35.1% of non-used bed nets were dirty or damaged, versus 8.3% for the LLINs. Conversely, a greater proportion of LLINs were not used for subjective reasons (18.6% versus 5.7%).

LLIN hang-up Day of the survey

Table 13: Hang-up of LLINs/bed nets the day of the survey Characteristics Bed nets/LLINs hung up Occupied covered sleeping spaces (%)

LLINs from the

distribution; N=5447 (%)

Other bed

nets/LLINs; N=1621 (%)

Among all

households; N=8593

Among households having at least 1 LLIN per sleeping

space; N=4346

Residential setting Urban

Rural

74.8 83.6

79.7 79.9

68.0 71.0

92.6 91.8

Phase of UC

First

Second

86.2

79.6

75.6

83.5

64.6

76.2

91.9

91.9

Wealth quintile

Poorest Second

Average

Fourth

Richest

85.8

82.9

86.0 80.5

78.4

73.0

76.7

86.3 87.0

75.1

73.0

72.2

69.8 73.2

65.4

94.3

90.9

91.1 94.8

88.3

Overall 82.5 79.9 70.6 91.9

Overall, on the day of the survey, 81.9% of the existing bed nets in the households were hung up and observed by the surveyors. The hang-up rate was 82.5% for LLINs from the distribution and 79.9% for other bed nets. The LLINs from the distribution were more likely to be hung up in rural households (83.6% versus 74.8%). Moreover, households in the first phase had hung up more LLINs compared with other bed nets (86.2% versus 79.6%). Conversely, a greater proportion of other bed nets were hung up in phase 2 (83.5% versus 75.6%). This could be explained by the fact that households tend to keep LLINs from the distribution for later and that in phase 1, fewer LLINs had been distributed.

Among the 1358 LLINs from the distribution that were not hung up the day of the survey, 788 (58%) were stored with or without their packaging, 513 (37.8%) were neither hung up nor stored, and 34 (2.5%) had been taken elsewhere.

The rate of sleeping spaces covered by a bed net was 70.6%, excluding sleeping spaces that were unoccupied the night before the survey. Higher rates were observed in rural settings (71.0%

30

versus 68.0%) and in phase 2 (76.2% versus 64.6%). When only accounting for the households that had achieved universal coverage, the rate of covered sleeping spaces was 91.9%, and this percentage was similar for both residential settings and both distribution phases.

Lastly, on the day of the survey, the surveyors directly observed 6149 bed nets hung up in households. Respondents also reported that 5710 bed nets were hung up the day before the survey. This suggests that only 439 nets had been taken down since the previous day, or 6.21% of the bed nets present in households.

Table 14: Number of LLINs hung up the day of the survey Characteristics Among households having at least 1 bed net (N=1469)

Households with at least 1 bed net

hung up (%)

Households with no bed nets hung

up (%)

Households with a few bed nets hung

up (%)

Households with all their bed nets

hung up (%)

Residential setting Urban

Rural

86.6

93.2

13.4

6.8

26.9

27.7

59.8

65.5

Phase of UC

First

Second

90.1

93.8

9.9

6.2

19.6

34.7

70.5

59.2

Wealth quintile

Poorest

Second Average

Fourth Richest

91.4

93.2

92.7

93.1

89.9

8.6

6.8

7.3

6.9

10.1

22.4

28.1

24.7

30.0

31.7

69.0

65.0

68.0

63.1

58.2

Overall 92.1 7.9 27.5 64.5

Among the households that had at least 1 bed net, 92.1% had at least 1 bed net hanging up and 7.9% had none hung up. Also, 64.5% of households had all of their bed nets hung up versus 27.5% that only had some bed nets hung up. These findings confirm the lower number of LLINs in rural households and in phase 1.

After distribution

Table 15: Time period for hanging up LLINs after distribution Characteristics For households that received at least 1 LLIN during distribution (N=1318)

Hung up at least 1

LLIN (%)

Among households that hung up at least 1 LLIN

(N=1214)

Hung up at least 1

LLIN within 2 days of distribution (%)

Hung up at least 1

LLIN within 1 week of distribution (%)

Residential setting Urban

Rural

91.1

93.1

61.7

59.2

96.2

96.4

Phase of UC First

Second

93.5

92.3

64.6

55.4

97.6

95.4

31

Wealth quintile

Poorest

Second Average

Fourth

Richest

95.1 92.1

93.9

92.3

90.9

61.8 61.1

60.1

62.4

52.6

96.3 94.7

98.8

97.7

94.4

Overall 92.8 59.6 96.4

Among the 1318 households that received at least 1 LLIN during distribution, 92.8% hung up at least 1 LLIN, and among these, the vast majority, or 96.4%, hung it up within one week following distribution, and 59.6% within the following 2 days. These findings demonstrate a high level of acceptance of LLINs after one week and suggest that the side effects of insecticide in LLINs, such as eye irritation, did not hinder hanging up LLINs on a large scale.

Table 16: Number of LLINs hung up after distribution Characteristics Among households that hung up at least 1 LLIN (N=1214)

Number of LINNs

hung up

(Average)

Household that

hung up all received LLINs (%)

Households that had problems

hanging up their LLIN (%)

Residential setting

Urban Rural

3.57 4.38

81.9 85.3

16.4 14.3

Phase of UC

First

Second

4.25

4.25

85.4

84.2

2.7

24.7

Wealth quintile

Poorest

Second Average

Fourth

Richest

4.56

4.23 3.86

4.12 4.50

90.6

84.3 86.1

81.0 82.6

4.6

10.7 15.8

18.7 22.6

Overall 4.25 84.8 14.7

Among the 1214 households that hung up at least 1 LLIN, 84.8% hung up all the LLINs they had received, and the average number of hung-up LLINs was 4.25 LLINs per household. The number of hung-up LLINs was significantly higher in rural settings, which is consistent with the larger size of these households. In addition, 14.7% reported problems hanging up the LLINs. A significantly higher proportion was noted among households in phase 2. This is explained by the rectangular shape of the LLINs distributed in phase 2, making hung-up more difficult than for circular LLINs. Similarly, the richest households were more likely to encounter difficulties in comparison to the poorest households.

Figure 5: Reported problems related to LLIN hang-up after distribution

INSERT FIGURE 5

Among the 188 households that reported problems when hanging up LLINs, the main problem was related to the shape or size of LLINs that did not fit. In rural settings, 78.9% of the problems were attributed to the size of the LLINs, compared to 50% in urban settings. By contrast, the shape of the LLIN seemed to cause more problems in urban settings with 27.1% compared to 3.1% in rural settings. Thus, it seems urban

32

households are more concerned about the appearance of their homes during the day and, thus, are more eager to have the circular LLINs that are easy to take down each morning.

Utilization of insecticide-treated bed nets the night before the survey

Table 17: Utilization of ITNs by population groups Characteristics Among the usual residents who slept at the house the previous night

Overall; N=15094 (%)

Under 5 years; N=2550 (%)

5–14 years; N=4290 (%)

Pregnant women; N=262 (%)

Residential setting Urban

Rural

65.1

69.2

68.2

72.6

64.3

64.0

66.3

74.7

Phase of UC

First

Second

61.8 75.3

63.9 79.8

57.3 70.8

64.8 79.7

Wealth quintile

Poorest

Second

Average

Fourth

Richest

70.2 72.4

66.9

70.0

63.9

73.3 79.1

66.8

72.2

69.6

63.4 66.2

65.3

66.4

58.7

76.9 80.3

72.5

71.7

71.7

Overall 68.6 72.2 64.0 73.5

The night before the survey, 68.6% of the usual and present residents had slept under an insecticide-treated net. Among children under five years, the percentage was 72.2%; among children 5 to 14 years, 64.0%; and among pregnant women, 73.5%. It is interesting to note that LLIN use is higher among groups traditionally considered to be vulnerable (pregnant women and children under five years). On the other hand, a significantly higher proportion of people have used their ITNs in the phase-2 regions, and this occurred for each population category, except among pregnant women. Lower proportions among the richest households were also noted.

40

Table 18: ITN utilization relative to household ITN coverage Characteristics Among usual residents who slept at the house the night before

(N=15094)

Overall Living in a household

having at least 1 LLIN (N=14505)

Living in a household

having at least 1 LLIN per sleeping

space

(N=5537)

Residential setting

Urban

Rural

65.1

69.2

69.0

71.2

89.4

90.4

Phase of UC

First

Second

61.8 75.3

65.5 76.0

92.1 89.1

Wealth quintile

Poorest

Second Average

Fourth

Richest

70.2 72.4

66.9

70.0

63.9

73.9 73.5

69.5

71.3

66.9

92.6 88.5

89.1

89.7

91.0

Overall 68.6 70.9 90.2

This analysis indicates that households with enough LLINs, in terms of the two universal coverage indicators (LLIN per sleeping space and LLIN per person), had significantly higher proportions of people who slept under ITNs the previous night. Furthermore, the differences observed above between the two phases disappeared among households with enough bed nets to ensure universal access.

Among households that have at least 1 LLIN per sleeping space, the majority, or 59.7%, have some members who used an LLIN the previous night, and nearly two-thirds of households, or 36.9%, had all household members sleeping under 1 LLIN. By contrast, only 3.3% had no household members sleeping under an LLIN. The average number of users per bed net was 1.83, and this average was significantly lower among the richest households when compared to the poorest households. Among the 962 households that have close to 1 LLIN per sleeping space, 61.5% had used all their LLINs, and only 2.9% had not used any. The average number of LLINs used was 4.36 per household, and it was not surprising to note that rural households had used more LLINs compared to urban households, probably reflecting the larger size of rural households.

Figure 6: Comparison of sleeping space habits based on having enough bed nets to ensure universal

access

INSERT FIGURE 6

Among the households that have achieved universal coverage (defined as 1 LLIN per sleeping space), 51.4% of ITNs were shared by several people. However, among the households that have 1 ITN per sleeping space, this percentage was 69.1%, and this difference is statistically significant.

This suggests that the population in the study regions changed their sleeping habits, based on the number of available bed nets.

Similarly, the average number of users per utilized INT was considerably lower among households

41

that had achieved universal coverage (1.65 versus 2.03), confirming that the population was significantly more likely to share a bed net in households that do not have enough of them.

Behavior change communication (BCC)

BCC coverage rate

Table 19: Household exposure to BCC Characteristics Received

infor-

mation

Information source (N=1126)

Campaign flyer

(%)

Radio (%) Health

worker (%)

Village leader,

family, friends (%)

Home visits (%)

Residential setting

Urban Rural

67.9 76.9

4.2 8.0

47.2 41.4

37.3 37.9

19.0 23.2

30.1 30.1

Phase of UC

First

Second

67.6

82.3

9.4

5.8

40.4

43.9

40.7

35.5

21.7

23.0

21.6

36.6

Overall 75.2 7.4 42.4 37.8 22.5 30.1

Three-fourths of households, or 75.2%, were exposed to BCC messages during the campaign. Overall, the main information sources were radio (42.4%); health worker (37.8%); home visits (30.1%); village leader, family, and friends (22.5%); and, lastly, the flyers (7.4%). Household exposure to information was significantly higher during the second phase with 82.3%. It is interesting to note that the order of popularity of the various sources of information was identical to that of phase 1, suggesting an overall improvement in communication activities. Lastly, more rural households had received the information.

Table 20: Message content related to LLIN use Characteristics Among household that received information N=1126 (%) Overall N=1541 (%)

“Use your bed

nets”

“Hang up your bed net”

“Sleep under

your bed net

every night”

Cites at least

1 message

about using

bed nets

Cites at least 1 message about using

bed nets

Residential setting Urban

Rural

39.4 44.1

38.1 38.7

46.8 48.5

81.3 82.1

55.3 63.1

Phase of UC

First

Second

49.5

38.6

38.8

38.5

45.7

50.2

83.1

81.0

56.2

66.7

Overall 43.3 38.6 48.2 81.9 61.6

Table 21: Message content related to LLIN maintenance Characteristics Among households that received information

N=1126 (%)

Overall

N=1541 (%)

“Recognize the

value of your bed net”

“Air out bed

net for 24h in

the shade

before using”

“Wash the bed

net with soap

and water”

Cites at least

1 message

about bed-net maintenance

Cites at least

1 message

about bed-net

maintenance

Residential setting Urban

Rural

18.1 18.0

49.0 55.4

27.8 28.1

65.0 70.3

44.1 54.0

42

Phase of UC

First

Second

18.9

17.4

42.8

63.1

18.6

35.2

60.0

76.6

40.5

63.1

Overall 18.0 54.3 28.0 69.4 52.2

Overall, 61.6% of households cited at least 1 message about using bed nets. This proportion was closely related to exposure to communication messages since, when excluding the households that had not received any information, 81.9% of the respondents could quote at least 1 message.

Also, 52.2% of all the households quoted one message on LLIN maintenance, and this proportion rose to 69.4% by excluding households that were not exposed to the communication messages. The most popular message was “Air out bed net for 24h in the shade before using” with 54.3%. The proportions were generally higher in rural settings and in phase 2.

The “Trois Toutes” slogan

Table 22: Exposure to the “Trois Toutes” Characteristics Households that

had heard or seen the phrase

the “trois

toutes” (N=1541)

Information source (N=496) Households

that understand the slogan’s

meaning

(N=496)

Radio (%) Television (%) T-shirt (%)

Residential setting

Urban

Rural

32.1

31.1

15.2

13.3

24.1

12.3

51.2

61.3

14.7

20.9

Phase of UC

First

Second

28.0

34.3

13.8

13.5

20.5

9.9

51.2

65.6

16.8

22.0

Region

Kaffrine Kaolack

Kolda

Sedhiou Kedougou

Tambacounda

27.5 38.9

25.7 24.0

24.1 31.1

15.4 12.6

3.3 21.1

48.2 11.6

6.8

11.4

6.2 20.2

35.7 23.9

57.3 69.7

79.4 54.2

10.7 44.9

22.7 21.6

11.5 32.9

0.0 15.3

Overall 31.2 13.6 14.5 59.4 19.8

For the entire sample, 31.2% had seen or heard the “Trois Toutes” slogan. T-shirts were mentioned as the main information source. Among the 496 respondents who had been exposed to the slogan, 19.8% knew its exact meaning. It is interesting to note that the region of Kedougou stood out from the other study regions in that radio seems to have been the main information source for the “Trois Toutes,” but none of the households knew its exact meaning.

Figure 7: Intention and perception about using bed nets INSERT FIGURE 7

The majority of respondents, or 83.5% of them, expressed their intention to use their LLINs every night. By contrast, only 55.9% thought that their neighbors used their LLINs every night. Next, the intention to use LLINs was analyzed relative to LLIN availability in households. It appeared

43

that the proportion of LLINs used every night the week before the survey (84.7%) was similar to respondents’ intention to use them year round. The intention to use LLINs year round was higher among households that had 1 LLIN per sleeping space (88.3% versus 79.2%) while the intention to use LLINs during the rainy season was higher among households that did not have enough LLINs to cover all their sleeping spaces (8.0% versus 5.2%). This suggests that these households would be more inclined to save their LLINs for the period of highest risk in terms of mosquito exposure.

Table 23: Intention to use LLINs and actual use the week before the survey

Intention to use LLINs Characteristics Among households that have achieved

UC; N=741

Among households that

have not achieved UC;

N=800

LLINs used every

night the week

before (%)

During the rainy season

(%)

During the

entire year

(%)

During the rainy season

(%)

Durant the

entire year

(%)

Residential setting Urban

Rural

4.1 5.4

90.4 87.8

7.9 8.0

78.6 79.4

83.0 85.0

Phase of UC

First Second

8.1

3.2

79.6

94.2

12.1

2.9

71.5

88.7

85.2

84.3

Overall 5.2 88.3 8.0 79.2 84.7

Other preventive measures against mosquitoes Table 24: Other preventive measures occasionally used by households Characteristics Aerosol

spray (%) Incense stick

(%) Fan (%)

Herbs or plants (%)

IRS within the last

12 months (%)

Residential setting

Urban Rural

35.6 19.2

41.5 35.0

28.4 2.1

16.9 28.0

3.3 8.2

Phase of UC

First

Second

23.9

20.8

35.5

36.8

7.4

6.6

23.5

28.2

5.0

9.4

Wealth quintile

Poorest

Second

Average Fourth

Richest

11.2 17.8

19.7 24.1

36.5

25.7 35.4

39.0 40.4

39.3

0.7 3.3

7.4 6.3

16.1

27.7 23.5

26.0 27.5

24.9

7.2 8.3

7.2 3.9

9.8

Overall 22.3 36.2 7.0 25.9 7.3

For all households, 36.2% occasionally used incense sticks, 25.9% used herbs or plants, 22.3% used an aerosol spray, 7.0% used a fan to protect themselves from mosquitoes, and 7.3% of households had been sprayed within the last 12 months. Several significant differences were detected. First of all, rural households were more likely to use herbs or plants while in urban settings, they were more likely to use a fan or aerosol sprays. Secondly, the use of aerosol sprays and incense seemed to increase with the level of economic status, and the households in the richest quintile were significantly more inclined to use a fan compared to other less well-off households.

Figure 8: Percentage of households that had all household members protected under an LLIN the previous night, among households with 1 LLIN per sleeping space (N=662)

44

INSERT FIGURE 8

When considering families with at least 1 LLIN per sleeping space, in the phase-1 regions, the households using other preventive measures were significantly more likely to have had all their members sleeping under LLINs the night before. By contrast, in phase 2, a higher percentage of households that use no other preventive measure had all their members protected by an LLIN the night before the survey. This difference between the two phases for LLIN utilization shows that populations in the phase-2 regions had been specifically sensitized on LLIN use, given that the proportion of households using other preventive measures remained similar.

Population’s level of knowledge and perceptions

Figure 9: Knowledge about the causes of malaria

Nearly all households (96.4%) reported mosquito bites as the cause of malaria. Beliefs related to ingesting food or drinks were: eating salty food: 6.8% (n=82); eating green mangoes: 2.1% (n=30); and drinking salt water: 4.2% (n=60). Beliefs related to climate conditions were as follows: drinking curdled milk during the rainy season: 3.0% (n=33); getting drenched in the rain: 9.6% (n=154); cold weather or a change in climate: 2.7% (n=36); and, lastly, the sun: 1.7% (n=28).

Figure 10: Knowledge about protective measures against malaria

INSERT FIGURE 10

Using bed nets was the primary method cited by households to prevent malaria. In fact, 92.6%, or 1444 households, responded, “sleep under a bed net” or “sleep under an insecticide-treated net.”

Table 25: Perception of malaria as a threat Investigated items Strongly

agree Agree Do not

agree Strongly disagree

Approval score

% % % % Average 95%CI

Malaria is the most serious health

problem in my community

47.8

44.5

7.0

0.7

1.32

1.21 – 1.42

45

Malaria cases have decreased in

recent years

40.8

47.8

7.9

3.8

1.13

0.99 – 1.27

People in this community only catch malaria

in the rainy season

21.5

39.5

33.0

6.1

0.37

0.23 – 0.51

People get malaria only where there are

many mosquitoes

36.7

43.8

15.5

3.9

0.94

0.83 – 1.05

Each year, many children from this

community get malaria

28.4

51.2

18.4

2.0

0.86

0.69 – 1.03

I don’t worry about malaria because it can be treated easily

8.3

24.6

45.5

21.6

-0.47

-0.60 - -0.34

Malaria can prevent me from working or earning money

65.0

33.1

1.4

0.6

1.60

1.54 – 1.66

Each year, my family spends a lot of money on healthcare due to malaria

34.7

50.0

14.5

0.8

1.03

0.88 – 1.19

Malaria can prevent my children from going to school

61.5

37.4

1.0

0.1

1.59

1.53 – 1.65

Malaria can slow a child’s growth

49.4

46.9

3.2

0.6

1.41

1.32 – 1.50

* The scores for agreement were 1 and 2 and -1 and-2 for disagreement.

School and work absenteeism seemed to be the most concerning threat for respondents; in general, the population expressed concern about the idea of getting malaria and seemed aware that they could get it during any season of the year.

Table 26: Perceptions about bed nets Investigated items Strongly

agree Agree Do not

agree Strongly disagree

Approval score

% % % % Average 95%CI

Insecticide on bed nets is not dangerous for children

20.9

39.2

29.9

10.0

0.31

0.16 – 0.46

It is difficult to sleep under a bed net when the weather is hot

22.1

42.2

28.3

7.4

0.43

0.33 – 0.53

Sleeping under a bed net is a good way to

have some privacy in a densely populated

house

22.8

54.7

12.3

10.2

0.68

0.48 – 0.87

Insecticide is not harmful to people who sleep

under a bed net

22.4

51.4

19.4

6.7

0.63

0.53 – 0.73

Many people in this region prefer not to sleep under a bed net

6.1

19.9

46.1

27.9

-0.70

-0.86 - -0.54

Many people in this region prefer

not to sleep under a bed net because of its color

3.5

14.8

51.3

30.4

-0.90

-1.02 - -0.79

Insecticide on bed nets is not dangerous for pregnant women

22.5

45.0

21.8

10.7

0.47

0.32 – 0.61

* The scores for agreement were 1 and 2 and -1 and-2 for disagreement.

Even though close to three-fourths of households believe that many people in the region would prefer to sleep under bed nets, it seems that significant proportions of households believe that the insecticide used in LLINs is harmful to users (21.1%), children (39.9%), or pregnant women (32.5%). On the other hand, even though 77.5% of households believe that bed nets are a good means of having privacy in a densely populated household, 35.7% of people believe that heat is a barrier to using bed nets.

Table 27: Perception of bed net efficacy

46

Investigated items Strongly

agree

Agree Do not

agree

Strongly

disagree

Approval score

% % % % Average 95%CI

Some people who sleep under bed nets

get malaria anyway

13.3

47.0

28.6

11.2

0.22

0.09 – 0.35

Sleeping under a bed net is the best

protection against malaria

61.7

36.1

1.6

0.6

1.57

1.51 – 1.62

New bed nets protect people from malaria for several years

40.1

47.0

11.1

1.8

1.12

1.02 – 1.23

Dead mosquitoes on the ground are a

good indication of bed net efficacy

53.1

43.9

2.5

0.5

1.47

1.40 – 1.53

Sleeping under a bed net is the best

protection against mosquitoes

58.2

40.5

0.9

0.4

1.55

1.51 – 1.60

It only takes a few months before a bed net gets a hole in it and lets mosquitoes through

10.0

23.9

47.6

18.5

-0.41

-0.55 - -0.27

Bed nets only protect against mosquito

bites if they are used with certain types of beds

16.9

45.0

28.7

9.3

0.31

0.15 – 0.48

Bed nets that are more expensive are more

effective than lower cost or free bed nets

10.6

17.4

38.7

33.2

-0.66

-0.81 - -0.52

* The scores for agreement were 1 and 2 and -1 and-2 for disagreement.

Overall, people in the study regions perceive LLINs as an effective means to control malaria. In effect, 97.8% think that sleeping under a bed net is the best protection against malaria. Nevertheless, several factors that may have a negative impact on LLIN utilization were identified through this analysis. Firstly, the vast majority of households agree that dead mosquitoes on the floor are a good indication LLIN efficacy. Next, close to 34% seem to think that in a few months, LLINs get holes and let mosquitoes through them. Lastly, over half of the sample, or 61.9%, think that LLINs are only effective with certain types of beds. Table 28: Confidence in the possibility of using bed nets Investigated items Abso-

lutely

could

Definitely could

Definitely could not

Unsure whether

could

Approval score

% % % % Average 95%CI

Hanging up a bed net over each of your children’s sleeping spaces

58.6

32.3

7.7

1.4

1.39

1.32 – 1.46

Hanging up a bed net over all sleeping spaces

46.4

41.7

10.4

1.4

1.21

1.12 – 1.31

Getting the entire family to sleep under

bed nets, every night of the year

52.7

31.9

13.5

1.9

1.20

1.10 – 1.30

Consistently using a bed net in rooms that are also used during the day

22.9

27.2

39.7

10.2

0.13

0.00 – 0.26

Sleeping under a bed net when there

are a lot of mosquitoes

69.5

27.5

2.2

0.8

1.63

1.57 – 1.68

Sleeping under a bed net when there

are not many mosquitoes

50.8

35.7

11.6

1.9

1.22

1.14 – 1.29

Getting all your children to sleep under bed nets, every night of the year

54.5

32.1

11.5

1.9

1.26

1.16 – 1.35

Getting a good night’s sleep while sleeping under a bed net

62.8

35.2

1.6

0.5

1.58

1.52 – 1.64

Convincing your spouse to sleep under

a bed net

61.3

35.5

2.8

0.4

1.54

1.48 – 1.61

* The scores for agreement were 1 and 2 and -1 and-2 for disagreement.

47

The respondents seemed confident in being able to sleep under bed nets if there were a lot of mosquitoes as well as being able to get a good night’s sleep under a bed net and convincing their spouse to sleep under the bed net. Nevertheless, close to 50% of households do not think they could consistently use a bed net in rooms that are also used during the day. This highlights the importance of easy hang-up of LLINs to limit this type of barrier to their use.

Figure 11: Frequency of discussion within the family about bed net use

INSERT FIGURE 11

Utilization of LLINs appeared to be a frequent topic of discussion among families; 93% reported discussing their use often or sometimes and 64% very often.

Figure 12: Odds ratio adjusted to the different variables on utilization for all LLINs INSERT FIGURE 12

Among the 636 households possessing at least 1 LLIN per sleeping space and excluding those with more than 1 LLIN per sleeping space, the families who expressed the intention to use their LLINs every or most nights were over 2.5 times more likely to have used all their LLINs the night before the study.

Improving the behavior change communication strategy between the two phases:

Several lessons were drawn from the experience of the first phase that led to improvements in the BCC strategy. While in the first phase each health district led the communication activities, the strategy was standardized by the central level during the second phase. One measure that had a significant impact was involving previously trained traditional communicators, from the time of registration until the home visits after distribution. Outreach communication has also been strengthened by setting up a monitoring committee made up of influential community members (such as the village leader or head nurse at the health post).

These improvements led to better results during phase 2. First, an increase of 14.7% (82.3%–67.6%) was noted for household exposure to information. Next, the proportion of households expressing their intention to use LLINs every night rose from 42.8% to 77.4%. Lastly, even though in phase 1 there were more households using all their LLINs among those that also used other preventive methods for malaria, in phase 2, there were more households using all their LLINs among those that do not use other preventive measures, indicating that the population in phase 2 had been sensitized specifically in the use of bed nets.

48

DISCUSSION OF METHODS

The sampling method used for this study is a classical approach of a stratified two-stage cluster sample design, as used during the DHS and MICS surveys. Household selection for each sample consisted of systematically identifying each household the day of the survey in order to select study participants through a simple randomization process. During analysis, the sampling weight proportional to the probability of cluster selection was incorporated into the database. In addition, the study did not use the registration list created during the distribution so that households that were potentially excluded from registration during distribution could still be selected. Therefore, the potential inclusion in the study of families that moved to the study regions after the distribution is the only limitation to the sampling process. Nevertheless, if these families moved within study regions, they would have received a visit through the distribution, and it is unlikely that the rate of trans-regional immigration is on a scale that could alter results. Lastly, in the event no adequate respondent was available at the household, the surveyors made three consecutive visits, but if unsuccessful, the household was dropped with no replacement to avoid introducing selection bias. As a result, the sampling method used for this study took into account the key elements necessary to ensure that the selected sample was an accurate representation of the study population.

Next, the study aimed to evaluate the sleeping-space registration strategy to achieve universal coverage in the regions covered by the first two phases of distribution. Achieving universal coverage is a direct result of estimating the gap in LLINs before distribution. However, during the distribution process, the gap in LLINs was estimated by the registration-data validation committee and, therefore, is not measured through the cross-sectional household survey method that was used in this research. As a result, the findings in this study made it possible to estimate the overall efficacy of the entire distribution process, including registration but does not allow for attributing the findings to the registration strategy as such. Furthermore, in order to account for existing coverage due to previous distribution campaigns targeting children under five years, this element was introduced during phase 2 during validation of registration data before distribution by using the following formula: (No. of persons living in household – No. of children ages 1 to 6 years)/2.” This procedure poses limitations on assessing the sleeping-space registration strategy since the formula takes into account the number of persons instead of the number of sleeping spaces. Validation of the results from the registration data was conducted at the health post level by a monitoring committee made up of members close to the community (for example, head nurse of the health post, village leader) who personally know the heads of household. As a result, the monitoring committee relied on objective elements (for example, number of household members significantly lower than the number of reported nighttime sleeping spaces) to adjust the estimation of LLIN needs. Introduction of this formula resulted in an 8% reduction in estimated LLIN needs in phase 2. In addition, comparing results by distribution phase made it possible to evaluate the relevance of introducing this corrective formula.

Moreover, it is likely households reported that discarded bed nets were damaged when they could have been classified as “usable” during the registration in order to receive more LLINs during the distribution. However, community health workers faced these same challenges, and it is logical to suspect that the proportion of older bed nets that were concealed was higher during registration than the theoretical calculation of the gap in LLINs. Nevertheless, it seems appropriate to consider that this possible bias is non-differential, suggesting that the proportions of households that received the exact (59.4%), excessive (11.0%), or insufficient (29.6%) number of LLINs during distribution are representative of the actual situation in the population.

49

Lastly, this study collected data on sleeping spaces. Given that distribution was targeted at sleeping spaces and that the definition of “sleeping space” is rather vague, checking the validity of collected data proved to be particularly important. On the one hand, the inclusion of these data allowed for in-depth checking of the validity of the information obtained by comparing the data for sleeping spaces, households, persons, and bed nets. It appeared that estimates for bed-net use were consistent at the various levels. On the other hand, the average number of persons per sleeping space when using the data reported by respondents (1.80) corresponded with the average number of persons per occupied sleeping space the night before the survey (1.81). Therefore, the validity of results relative to sleeping spaces and bed-net use seems solid and free of information bias on a scale that would invalidate study conclusions.

50

CONCLUSION

Context and objectives

As noted, Senegal opted to implement an innovative strategy to achieve universal LLIN coverage. This strategy consists of registering households, sleeping spaces, and usable LLINs; allocating new LLINs to sleeping spaces that had none, household by household; and identifying the LLINs distributed during the 2009 campaign for children under five years, previous campaigns, and routine distribution. The goal was to achieve coverage of 75% of households having 1 LLIN per sleeping space and 75% of sleeping spaces covered with an LLIN so that 85% of the population is sleeping under an LLIN.

This study aimed to assess the LLIN distribution strategy in the target regions by measuring the

efficacy of this intervention by examining the various steps, including registration, distribution,

and behavior change communication (BCC). The goal of this study was to determine whether the

sleeping-space registration strategy achieved universal coverage within a 10% margin and if it

effectively closed the gap in household coverage, with less than 10% of households over or under

supplied.

Key results

The registration covered 90.5% of households, and 79.0% of all households with a gap of at least 1 LLIN before distribution received a voucher. For 96.3% of the visited households, all the sleeping spaces were registered. The participation rate at the distribution was 92%. As a result, 90.1% of households that needed at least 1 LLIN benefited from the distribution. In addition, the majority of households, or 70.4%, that needed at least 1 LLIN and benefited from the distribution received enough LLINs to cover their sleeping spaces within a margin of 1 LLIN per household. Household size has proven to be a determining factor in supplying the correct number of LLINs, with households under 8 persons more likely to receive the correct number relative to their needs.

The results of this distribution were a net increase (38.8%) in coverage for households having 1 LLIN per sleeping space between the distribution and the day of the survey. Therefore, coverage for households having 1 LLIN per sleeping space on the day of the survey was estimated at 41.9%. The average number of LLINs was 0.74 per sleeping space and 0.43 per person. Lastly, the retention rate of LLINs distributed 3 to 12 months before the survey was quite good (95.2%). By contrast, only 49.0% of bed nets procured before distribution were still present in the households the day of the survey.

On the day of the survey, 82.5% of the distributed LLINs were found hanging over sleeping spaces. This rate was 79.9% for older bed nets. The rate for covered sleeping spaces was 70.6%, and, excluding households that had not achieved universal coverage, this rate was 91.9%. Also, 96.4% of households had hung up at least 1 LLIN within the week following distribution.

Next, 68.6% of the population had used an LLIN the night before the survey and this percentage was 72.2% among children under five years and 73.5% among pregnant women. These estimates

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were dependent on LLIN coverage; among the households that had 1 LLIN per sleeping space, 90.2% of the population had used an LLIN the night before. Moreover, the percentage of LLINs shared by at least 2 people was significantly higher (69.1% versus 51.4%) among households that do not have enough LLINs to cover all their sleeping spaces, indicating that people altered their sleeping habits based on LLIN availability during the rainy season.

Coverage for BCC activities was 75.2%. The respondents widely expressed (83.5%) their intention to use their LLINs every night. Also, the level of knowledge among people living in the study regions was high; 96.4% of households mentioned mosquito bites as the cause of malaria, and 92.6% mentioned using a bed net to prevent infection. Some barriers to using LLINs were identified, such as the belief by over one-third that insecticide was harmful to children and pregnant women or that an LLIN was effective only with certain types of beds (60% of households).

Comparisons based on residential setting consistently showed more encouraging results among rural households, indicating that it was significantly easier to implement distribution activities in villages. Community involvement was significantly higher in rural settings and further facilitated by sensitizing the community and acceptance of community health workers in households. Moreover, it was more likely that rural households would have an available member on the day of registration or distribution. By contrast, the rural households were significantly larger (11.8 versus 8.6 members) and were, therefore, more likely to receive enough LLINs than urban households. Lastly, the results for the phase-2 regions were also consistently better, reflecting operational improvements due to capitalizing on lessons learned during phase 1.

This study highlighted several significant elements:

The first two phases of LLIN distribution were very effective (Table 6) in supplying

households with LLINs (distribution efficacy rate: 90.1%).

Even though distribution led to a significant increase (41.9% versus 3.1%) in coverage

of households that have at least 1 LLIN per sleeping space (Table 10), the implemented strategy did not achieve universal coverage within a 10% margin (Table 8). Although 70.4% of the households had received enough LLINs to cover their needs the day of the distribution, 41.9% coverage for households that had 1 LLIN per sleeping space the day of the survey was below the projected goal of 75%.

Mass distribution of LLINs was appropriate because nearly all households needed to

receive at least 1 LLIN in order to cover all their sleeping spaces. Nevertheless, this resulted in a high percentage of older LLINs that were discarded after distribution (51.0%) compared to the LLINs (4.8%).

On the day of the survey, the rate of LLIN use was similar to that of retained older LLINs (82.5% versus 79.9%). Overall, 70.6% of sleeping spaces actually occupied the night before were protected by an LLIN (Table 13).

The night before the survey, 68.0% of the population had slept under an LLIN (Table

17). When only considering the households that had achieved universal coverage, this percentage was 90.2%, or significantly higher than the goal of 85%. Therefore, this study has demonstrated that universal LLIN coverage for the population is achievable, if enough LLINs are available.

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The proportion of households where all members were protected by an LLIN was higher among households that did not use any other preventive measure in phase 2 (70.8% versus 58.55) while the opposite was observed in phase 1 (36.8% versus 59.1%). This indicates that the population in the phase-2 regions had been sensitized specifically about LLIN use and reflects the efficacy of BCC activities (Figure 8).

In conclusion, the goal was to determine if the sleeping-space registration strategy achieved universal coverage within a 10% margin and if it effectively closed the gap in household coverage, with less than 10% of households over or under supplied. This research has demonstrated that the two preliminary distribution phases effectively covered 70.6% of sleeping spaces the night before the survey. Even though a lack of available LLINs had been a challenge during the first phase, the observed trend was households with an under supply of LLINs relative to their number of uncovered sleeping spaces.

Recommendations

A routine LLIN distribution system should be implemented quickly in order to maintain the achieved coverage. People’s high level of motivation to use LLINs as well as their good knowledge about malaria prevention suggests that a pull-system would be appropriate in that households could then estimate their own LLIN needs and cover their gaps at their earliest convenience.

Distribution activities should take into account community members’ availability to guarantee maximum efficacy. The registration teams should adapt their work schedules to those of people in urban settings. Similarly, organization of distribution sites should also consider people’s availability.

Needs estimation for LLINs should be based on the number or sleeping spaces without deducting the number of existing LLINs to avoid under-supplying households by counting the LLINs that were distributed in 2010, which are probably already at the end of their usefulness.

Behavior change communication activities should be strengthened relative to LLIN maintenance. They should also target misconceptions about the efficacy of LLINs and the toxicity of insecticide.

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REFERENCES

Final results from the third General Census of Population and Housing (2002), National Agency

for Statistics and Demography (ANSD in French), National Report Presentation, June 2008.

Guide méthodologique du Programme National de Lutte contre le Paludisme du Sénégal, 2010

Sénégal: Enquête Nationale sur le Paludisme, 2008 – 2009 (ENPS-II); Salif Ndiaye, Centre de

Recherche pour le Développement Humain and ICF Macro, Calverton, USA, July 2009.

The Alliance for Malaria Prevention: a toolkit for mass distribution campaigns to increase

coverage and use of long-lasting insecticide-treated nets.

Thawani N, Kulkarni MA, and Sohani S. (2009). Factors associated with coverage and usage

of Long-Lasting Insecticidal Nets in Madagascar. Journal of Tropical Medicine, doi: 10.1155/2009/451719.

Vyas S, Kumaranayake L. How to do (or not to do)… Constructing socioeconomic indices:

How to use principal components analysis. Health Policy and Planning. 2006 November: 21(6): 459–68

Kakwani NC, Wagstaff A, van Doorslaer E: Socioeconomic inequalities in health: measurement,

computation, and statistical inference. J Econometrics 1997, 77:87–103

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ANNEX

Comparison of key indicators for distribution with other countries

Country

Senegal (2011)

Uganda (2010)

South Soudan

(2010)

Ghana (2010)

Nigeria Sokoto

(2010)

Season when data was

collected

End of dry

season

Start of rainy

season

Start of rainy

season

End of rainy

season

End of dry

season

Objective 1 LLIN per

sleeping

space

Universal

coverage

Universal

coverage

1 LLIN per

sleeping

space

2 LLINs per

household

Strategy Registration of

sleeping

spaces and

existing LLINs

Campaign

using

distribution

sites

Campaign

using

distribution

sites

Campaign for

hanging up LLINs

Campaign

using

distribution

sites

Number of persons per

sleeping space

1.80

1.90

1.52

1.40

Registration coverage 90.5% 92.4% 93.9% 66.1% 66.5%

Participation rate 92.0% 90.6% 65.5%

Campaign efficacy 90.1% 89.3% 88.4% 85.0% 61.9%

Coverage of households having at least 1 LLIN the day

of the survey

93.9%

91.6%

93.6%

82.1%

63.8%

Coverage of households having enough LLINs the day

of the survey

41.9%

68.9%

69.7%

19.9%

17.1%

LLIN retention rate 95.2% 94.5% 90.4% 73.0% 98.6%

LLIN utilization:

Overall

< 5 years Pregnant women

68.6%

72.2%

73.5%

66.2%

68.0%

-

-

-

-

34.3%

52.0%

39.4%

-

13.9%

16.7%